BEST-COST GitHub repository

Let’s start by checking out the BEST-COST GitHub repo and the README file

README

The README file contains the following information

• GET STARTED WITH THE healthiar R PACKAGE

• Initial installation of the healthiar package from GitHub

• TERMS OF USE

• NOTE

• DISCLAIMER

About healthiar

The main function family of healthiar is the attribute family, used to attribute health impacts to a (environmental) risk factor, e.g. noise or air pollution. The core function attribute_health can be used for most assessments.

Figure: healthiar overview

healthiar in RStudio 1/3

RStudio startup screen

healthiar in RStudio 2/3

Post installation, you can access the healthiar package landing page in RStudio by going to the Packages tab and then clicking on the healthiar package.

healthiar in RStudio 3/3

note: first screenshot of full screen note: mention vignette opens as pdf or html

Landing page of the healthiar package in RStudio, where you find the package vignettes and function documentation.

healthiar in RStudio - Vignette (3/3)

Intro vignette. You can open the intro_to_healthiar vignette within RStudio or as a HTML within your browser.

Fun doc

note: show fun doc of attribute_healthiar

Workflow BoD

note: quickly introduce the main workflow of burden assessments

Function families

note: Quickly show function families

Example: attribute call without input uncertainties

Goal: attribute COPD cases to air pollution

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear", # Alternatives: "linear", "log_log"
    rr_central = 1.369, 
    rr_increment = 10,  # μg / m^3
    exp_central = 8.85, # μg / m^3
    cutoff_central = 5, # μg / m^3
    bhd_central = 30747 # baseline health data: COPD incidence
  ) 

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear",
    rr_central = exdat_pm_copd$relative_risk, 
    rr_lower = exdat_pm_copd$relative_risk_lower,
    rr_upper = exdat_pm_copd$relative_risk_upper,
    rr_increment = 10, 
    exp_central = exdat_pm_copd$mean_concentration,
    cutoff_central = exdat_pm_copd$cut_off_value,
    bhd_central = exdat_pm_copd$incidents_per_100_000_per_year/1E5*exdat_pm_copd$population_at_risk,
    # bhd_central = exdat_pm_copd$incidence # Uncomment once change committed to main
  ) 

Output structure

Every attribute output consists of two lists (“folders”)

• health_main contains the main results

• health_detailed detailed results (and in some cases even more information about the assessment/calculation)

note: add here that format is tibble (but can be changed to data frame)

note: screenshot of how to click on variable in env (put that as first option) . . .

Different ways to access the results

Let’s check the main output!

results_pm_copd[["health_main"]]

Example: attribute with input uncertainties

Goal: attribute lung cancer deaths to PM2.5 exposure

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear",
    rr_central = 1.369, 
    rr_lower = 1.124, 
    rr_upper = 1.664,
    rr_increment = 10, 
    exp_central = 8.85, 
    exp_lower = 8, 
    exp_upper = 10,
    cutoff_central = 5,
    bhd_central = 30747, 
    bhd_lower = 28000, 
    bhd_upper = 32000
  ) 

Let’s check the detailed output!

results_pm_copd[["health_detailed"]][["raw"]]

Example: attribute with exposure categories (noise)

Goal: attribute cases of high annoyance (HA) to noise exposure

exdat_noise_ha <-
  exdat_noise_ha |>
  dplyr::filter(!is.na(exdat_noise_ha$exposure_mean))

results_noise_ha <- 
  healthiar::attribute_health(
    approach_risk = "absolute_risk",
    exp_central = c(57.5, 62.5, 67.5, 72.5, 77.5),
    population = sum(exdat_noise_ha$population_exposed_total), # TO DO: hard code input here
    prop_pop_exp = exdat_noise_ha$population_exposed_total / 
      sum(exdat_noise_ha$population_exposed_total),
    erf_eq_central = "78.9270-3.1162*c+0.0342*c^2")

results_noise_ha[["health_detailed"]][["raw"]] |> knitr::kable()

Iteration example

Compare example

note: definitely include because WP5 will do